SmartSharing: A CDN with Smart Contract-based Local OTT Sharing
Bibliographic record
Abstract
A content delivery network (CDN) uses distributed cache servers to reduce the content delivery latency to end users. In recent years, CDN providers adopt a new content caching strategy that allows end users to share their storage/bandwidth resources. Two core questions need to answer in this strategy: (1) how to incentivize end users to contribute their resources? (2) how to facilitate transparent, secure content trading among end users?We propose a new CDN solution, called SmartSharing, where users contribute their over-the-top (OTT) devices as mini-cache servers. To incentivize end users to contribute resources, SmartSharing uses game theory and an Expectation-Maximization (EM) algorithm to determine the content delivery schedule and the pricing scheme. To facilitate content trading among end users, SmartSharing uses smart contracts in Ethereum to create a transparent and safe transaction platform. We thoroughly evaluate the performance of SmartSharing with real-world trace-driven simulation as well as a prototype using content metadata and the derived pricing scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".